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import torch.nn as nn
import torch
from einops import rearrange
import numpy
def index_points(device, points, idx):
"""
Input:
points: input points data, [B, N, C]
idx: sample index data, [B, S]
Return:
new_points:, indexed points data, [B, S, C]
"""
B = points.shape[0]
view_shape = list(idx.shape)
view_shape[1:] = [1] * (len(view_shape) - 1)
repeat_shape = list(idx.shape)
repeat_shape[0] = 1
# batch_indices = torch.arange(B, dtype=torch.long).to(device).view(view_shape).repeat(repeat_shape)
batch_indices = torch.arange(B, dtype=torch.long).cuda().view(view_shape).repeat(repeat_shape)
new_points = points[batch_indices, idx, :]
return new_points
def knn_l2(device, net, k, u):
'''
Input:
k: int32, number of k in k-nn search
net: (batch_size, npoint, c) float32 array, points
u: int32, block size
Output:
idx: (batch_size, npoint, k) int32 array, indices to input points
'''
INF = 1e8
batch_size = net.size(0)
npoint = net.size(1)
n_channel = net.size(2)
square = torch.pow(torch.norm(net, dim=2,keepdim=True),2)
def u_block(batch_size, npoint, u):
block = numpy.zeros([batch_size, npoint, npoint])
n = npoint // u
for i in range(n):
block[:, (i*u):(i*u+u), (i*u):(i*u+u)] = numpy.ones([batch_size, u, u]) * (-INF)
return block
# minus_distance = 2 * torch.matmul(net, net.transpose(2,1)) - square - square.transpose(2,1) + torch.Tensor(u_block(batch_size, npoint, u)).to(device)
minus_distance = 2 * torch.matmul(net, net.transpose(2,1)) - square - square.transpose(2,1) + torch.Tensor(u_block(batch_size, npoint, u)).cuda()
_, indices = torch.topk(minus_distance, k, largest=True, sorted=False)
return indices
class Residual(nn.Module):
def __init__(self, fn):
super().__init__()
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(x, **kwargs) + x
class PreNorm(nn.Module):
def __init__(self, dim, fn):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.fn = fn
def forward(self, x, **kwargs):
return self.fn(self.norm(x), **kwargs)
class FeedForward(nn.Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(nn.Module):
def __init__(self, dim, heads = 4, dropout = 0.):
super().__init__()
self.heads = heads
self.scale = dim ** -0.5
self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(dim, dim),
nn.Dropout(dropout)
)
def forward(self, x, mask = None):
b, n, _, h = *x.shape, self.heads
qkv = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
if mask is not None:
mask = F.pad(mask.flatten(1), (1, 0), value = True)
assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
mask = mask[:, None, :] * mask[:, :, None]
dots.masked_fill_(~mask, float('-inf'))
del mask
attn = dots.softmax(dim=-1)
out = torch.einsum('bhij,bhjd->bhid', attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
out = self.to_out(out)
return out
class Local_Attention(nn.Module):
def __init__(self, dim, heads = 4,knn=4, dropout = 0.):
super().__init__()
self.heads = heads
self.scale = dim ** -0.5
#self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
self.q=nn.Linear(dim,dim,bias=False)
self.k=nn.Linear(dim,dim,bias=False)
self.v=nn.Linear(dim,dim,bias=False)
self.to_out = nn.Sequential(
nn.Linear(dim, dim),
nn.Dropout(dropout)
)
self.knn=knn
def forward(self, x, mask = None):
b, n, _, h = *x.shape, self.heads
point=x*1
X=x*1
idx = knn_l2(0, point.permute(0,2,1), 4, 1)
feat=idx
new_point = index_points(0, point.permute(0,2,1),idx)
group_point = new_point.permute(0, 3, 2, 1)
_1,_2,_3,_4=group_point.shape
q=self.q(X.reshape(_1*_2,1,_4))
k=self.k(torch.cat([group_point.reshape(_1*_2,self.knn,_4),X.reshape(_1*_2,1,_4)],dim=1))
v=self.v(torch.cat([group_point.reshape(_1*_2,self.knn,_4),X.reshape(_1*_2,1,_4)],dim=1))
q, k, v = rearrange(q, 'b n (h d) -> b h n d', h = h),rearrange(k, 'b n (h d) -> b h n d', h = h),rearrange(v, 'b n (h d) -> b h n d', h = h)
attn_map=q@k.permute(0,1,3,2)*self.scale
attn_map=attn_map.softmax(dim=-1)
out=attn_map@v
out=out.view(b,out.shape[0]//b,out.shape[1],out.shape[3]).permute(0,2,1,3)
out = rearrange(out, 'b h n d -> b n (h d)')
out = self.to_out(out)
return out
class Transformer(nn.Module):
def __init__(self, dim, depth, heads, mlp_dim, group=5, dropout=0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
Residual(PreNorm(dim, Attention(dim, heads = heads, dropout = dropout))),
Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
]))
self.group=group
def forward(self, x, mask = None):
bs_gp,dim,wid,hei=x.shape[0],x.shape[1],x.shape[2],x.shape[3]
bs=bs_gp//self.group
gp=self.group
x=x.reshape(bs,gp,dim,wid,hei)
x=x.permute(0,1,3,4,2).reshape(bs,gp*wid*hei,dim)
for attn, ff in self.layers:
x = attn(x, mask = mask)
x = ff(x)
x=x.reshape(bs,gp,wid,hei,dim).permute(0,1,4,2,3).reshape(bs_gp,dim,wid,hei)
return x
class Transformer__local(nn.Module):
def __init__(self, dim, depth, heads, mlp_dim,knn_k=4, group=5, dropout=0.):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([
Residual(PreNorm(dim, Local_Attention(dim, heads = heads,knn=knn_k, dropout = dropout))),
Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
]))
self.group=group
def forward(self, x, mask = None):
bs_gp,dim,wid,hei=x.shape[0],x.shape[1],x.shape[2],x.shape[3]
bs=bs_gp//self.group
gp=self.group
x=x.reshape(bs,gp,dim,wid,hei)
x=x.permute(0,1,3,4,2).reshape(bs,gp*wid*hei,dim)
for attn, ff in self.layers:
x = attn(x, mask = mask)
x = ff(x)
x=x.reshape(bs,gp,wid,hei,dim).permute(0,1,4,2,3).reshape(bs_gp,dim,wid,hei)
return x |